FairValue

Checking model

Esc

  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
  • EnvironmentTerminalExplore, compare and track players; market, league and model views.
  • EnvironmentStoriesValuations, head-to-heads and research findings, told as stories.
  • TerminalPlayer ExplorerSearch, filter and sort the player export.
  • TerminalComparison StudioTwo to four players on one scale.
  • TerminalWatchlistsNamed collections, kept in this browser.
  • TerminalMarket LandscapeHow observed value is distributed, and where the model drifts.

↑ ↓ to move · Enter to open · Esc to closeLoading player index…

Patrick

Egelund

CFDenmark · FC Fredericia

№ 422936Emerging €519k −1.24

Player dossierSuperliga

PatrickEgelund

CFCentre-ForwardAttack

Club
FC Fredericia
League
Superliga
Nationality
Denmark
Age
25
Height
169 cm
Foot
Right
Valued
25 May 2026

Reading the valuation

The market values Egelund at €150k. Model A, which never saw Egelund during training, estimates €519k: the market value is 71% below that estimate.

That is a factor of ×3.46. Model A's typical held-out miss is ×1.50 either way, so this gap is larger than its usual error.

Without league and club, Model B estimates €771k, further from the market. Model B is usually further off (typical miss ×2.07): league and club explain much of how the market prices players.

A gap describes the model's arithmetic for this player's inputs, not what the player is worth.

▼ −1.24A €519k▼ −1.64B €771k
Pitch length is a log value scale fitted to this player's figures. ● observed · ◇ Model A · dashed ◇ Model B · each arc is a residual, log(observed) − log(estimate).
Apps
13
Minutes
529
Goals
3
Assists
1
Started
6/13
Min / app
41
Cards
0Y 0R

Club matches, two years to the valuation date

The Valuation Journey

What moves Model A

Asking the prediction API to explain Model A for Egelund…

SHAP describes the model's arithmetic for this player's inputs, not what causes market value. Model B can be explained only by the live service; Model A also has a published explanation for the research players.

Model A vs Model B against the observed €150k

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €150kModel A €519k

Asking the prediction API…

Held-out estimates come from cross-validation, with this player outside the training data: the honest comparison. Served models were refitted on every player, this one included, so they sit closer to the market. Shaded band: Model A's typical miss (×1.50 either way).

Player traitspercentiles vs 1,583 attackers

Patrick Egelund's percentiles among attackers: Goals per 90 89th, Assists per 90 59th, Minutes, two years 3rd, Starts 29th, Minutes, last 365 days 16th, European club minutes 28th89thGoals59thAssists3rdMinutes29thStarts16thRecent28thEurope

Dashed ring: the attacker median (50th percentile)

Goals per 90

Club goals per 90 minutes played in the two years to the valuation date.

0.51 this player0.26 attacker median89th percentile

0.00peer deciles · median line · this player ●1.17

No passing, progression or defensive actions exist in the dataset, so no trait claims to measure them.

Valuation history every Transfermarkt valuation on record

First valuation
€250k
Dec 2019
Peak
€250k
Dec 2019
Model target
€150k
May 2026
Valuations
14
1 club change
May 2026€150kFC FredericiaValuation the model is trained to reproduce

May 2026: €150k, FC Fredericia. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2019-12-22€250kEsbjerg fB
2020-07-31€250kEsbjerg fB
2020-12-23€200kEsbjerg fB
2021-06-04€200kEsbjerg fB
2021-12-28€200kFC Fredericia
2022-06-20€250kFC Fredericia
2022-12-22€200kFC Fredericia
2023-06-26€150kFC Fredericia
2023-12-21€150kFC Fredericia
2024-06-18€100kFC Fredericia
2024-12-09€100kFC Fredericia
2025-06-16€200kFC Fredericia
2025-12-15€200kFC Fredericia
2026-05-25€150kFC Fredericia

Audit context group-level, not about this player

Audit group
UEFA
Population gap, Model A
+0.093 95% CI +0.063 to +0.124
This player, market vs held-out A
−1.241

The group comes from first-listed citizenship and is never a model input. Across the population, non-UEFA players' market values sit about 10% above Model A's held-out estimates, relative to UEFA players in the same leagues. One player's residual says nothing about that pattern (typical miss ×1.50), and none of it is evidence of discrimination: the target may itself be biased. Open the Fairness Audit

In context observed value among comparable players in this build

All players in the Terminal →

Superliga256 players · median €800k

Valued above 6% of the 256 Superliga players.

€20k€150k€1M€7.5M€150kSee them in the Explorer →

Attackers, all leagues1,583 players · median €2.5M

Valued above 2% of the 1,583 attackers.

€20k€300k€5M€75M€150kSee them in the Explorer →

Where Egelund appears

Watchlists and the comparison tray are kept in this browser only.

Near this valuation closest observed values: same league, then same position

Counts and ranks of observed Transfermarkt values only; model errors are never ranked here. Full player export: Every player in the modelling population (D-011).